Jobs at Databricks

Build and productionize data and AI solutions for federal customers using Databricks. Own architecture, design decisions, data pipelines, ML/AI integrations, and user-facing applications. Lead technical delivery, advise customers, integrate client systems, provide training, and collaborate with engineering, product, support, and project teams. Contribute reusable frameworks and best practices while managing complex stakeholder relationships and delivering secure, scalable production systems.

Customer-facing senior engineer who architects, implements, and productionizes end-to-end data and AI solutions on Databricks. Responsibilities include Spark-based distributed data engineering, ML/AI model integration, CI/CD deployments, technical project delivery, stakeholder engagement, reusable assets creation, and providing product/implementation feedback.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery, architecture reviews, agile delivery, testing, production readiness, launch, adoption, and strategic handoff. Build C-suite relationships, translate business needs into technical requirements, resolve delivery risks, and identify future high-value opportunities.

Lead customer-facing technical engagements for Databricks, including discovery, solution design, proofs of concept, platform demonstrations, architecture reviews, and technical strategy. Build production-quality solutions using data engineering, analytics, and machine learning technologies. Partner with account executives to expand platform adoption, explain competitive differentiation, and create reusable technical assets such as notebooks and reference architectures.

Leads Databricks’ global customer enablement partnerships, owning strategy, operating models, commercialization, partner offerings, pricing, governance, quality standards, and revenue growth. The role coordinates Legal, Finance, Curriculum, Training Operations, Commercialization, and regional teams while scaling partner-led curriculum, certification, and delivery programs. It also develops regional capabilities, manages global partnership ecosystems, and champions AI-enabled expansion.

Leads technical customer engagements for Databricks strategic accounts, including discovery, solution architecture, platform demonstrations, proofs of concept, and technical strategy. Builds production-quality data solutions across engineering, analytics, and machine learning; presents technical differentiation; develops reusable assets; and partners with Account Executives to expand platform adoption and consumption.

Lead and grow an enterprise sales team responsible for quota attainment, new-logo acquisition, Databricks usage growth, forecasting, and customer outcomes. Build trusted customer and partner relationships, develop enterprise territories, promote value-based selling, and foster a high-performance culture. The role requires leading account executives in Big Data, cloud, analytics, and AI markets while supporting regional growth plans and partner ecosystems.

Drive enterprise sales of Databricks’ Data Intelligence Platform to Fortune 500 and telecommunications accounts. Build executive relationships, expand usage across business units, manage strategic opportunities, exceed pipeline and revenue targets, forecast in Salesforce, and lead value-based solution selling. The role requires expertise in big data, cloud, SaaS, AI, and consumption-based sales motions, along with the ability to translate technical capabilities into business value.

Leads IT compliance for usage-based revenue processes, including platform consumption controls, cloud partner reconciliations, SOX-ready engineering workflows, and paid-usage metering data governance. Partners with Engineering, Finance, Accounting, and Internal Audit to resolve discrepancies, assess revenue recognition impacts, automate reconciliation processes, and maintain reliable revenue datasets.

Leads Order to Revenue process compliance for a usage-based revenue engine. Oversees usage remediation, root-cause analyses, partner reporting, metrics, financial reserves, revenue recognition adjustments, SOPs, and cross-functional collaboration with Engineering and business teams. Ensures operational execution aligns with compliance requirements and partnership terms while improving financial accuracy and resolving recurring issues.

Leads order-to-revenue compliance for usage-based revenue, overseeing metering, billing controls, product launches, partner royalty reconciliations, SOX compliance, audits, technical accounting, and issue remediation. Manages functional leads and partners with Engineering, Finance Data Systems, Sales Operations, FP&A, executives, and auditors to validate monetization processes, resolve metering issues, and ensure accurate revenue conversion.

Build and productionize customer-facing data and AI solutions on the Databricks Platform. Own architecture, design, implementation, deployment, and integration of data pipelines, distributed systems, ML/AI models, and applications. Lead technical delivery, advise enterprise stakeholders, train customers, resolve implementation issues, and contribute reusable frameworks and product feedback. This role requires customer immersion, cross-functional collaboration, and travel to customers approximately 20% of the time.

Build and productionize customer data and AI solutions on the Databricks Platform. Own architecture, design, development, deployment, and integration of scalable data pipelines, ML/AI models, and applications. Collaborate with customers, engineering, product, and project teams; provide technical guidance, training, documentation, and implementation support. Deliver measurable project outcomes, resolve technical issues, create reusable accelerators, and influence product direction. Travel to customers approximately 20% of the time.

Lead technical strategy for customer accounts, design scalable data engineering and real-time analytics architectures, and drive Databricks adoption. Advise senior customer stakeholders, lead architecture discussions, develop custom solutions for competitive opportunities, coordinate cross-functional teams, and influence product direction. The role requires deep expertise in Spark, Kafka, data warehousing, cloud platforms, Python, SQL, distributed systems, and production deployments. Travel may reach 30%.

Design and deploy production ML/AI architectures on the Databricks platform, lead Generative AI (LLM, RAG, agent) implementations, optimize and scale MLOps pipelines, provide technical pre-sales support, and translate customer feedback into product improvements while mentoring peers and meeting delivery milestones.

Advise enterprise customers on designing, securing, administering, and deploying Databricks environments on Microsoft Azure. Architect scalable cloud infrastructure, networking, identity, disaster recovery, observability, and infrastructure-as-code solutions. Support pre-sales activities through proof-of-concept architectures and technical content, while sharing expertise through training, tutorials, hackathons, conferences, and open-source contributions.

Customer-facing technical leader who architects, builds, and optimizes large-scale data engineering and lakehouse solutions on Databricks. Supports pre-sales and delivery with POCs, performance/load testing, workload sizing, migrations, observability, and adoption through workshops and community engagement.

Own the vision, strategy, and roadmap for Databricks’ AI-first learning platform. Build contextual guidance, adaptive learning paths, learner models, assessments, skills graphs, analytics, and personalization tools. Partner with engineering, UX, curriculum, education, and go-to-market teams to deliver scalable learning experiences for customers, partners, and employees. Define metrics, understand skills gaps, engage users, and improve platform adoption through embedded learning capabilities.

Lead technical strategy for customer accounts by designing scalable data, AI, streaming, and analytics architectures on Databricks. Advise senior technical stakeholders, drive platform adoption and consumption growth, conduct architecture discussions, build competitive solutions, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL coding, public-cloud deployment experience, distributed data systems expertise, and excellent technical communication.

Leads and scales a customer-facing AI Forward Deployed Engineering team, overseeing hiring, mentoring, strategic AI initiatives, enterprise engagements, and production AI delivery. Builds executive customer relationships, partners with Sales, Product, Engineering, and GTM, influences product strategy through customer insights, manages account and services outcomes, and represents Databricks as an AI thought leader.